Use of machine learning for classification of magneto cardiograms

Abstract

The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering.

1. A method for automating the identification of meaningful features and the formulation of expert rules for classifying magnetocardiography data, comprising:
 applying a wavelet transform to sensed data acquired from sensors sensing magnetic fields generated by a patient's heart activity, resulting in wavelet domain data; 
 applying a direct kernel transform to said wavelet domain data, resulting in transformed data; and 
 identifying said meaningful features and formulating said expert rules from said transformed data, using machine learning. 
 
     
     
       2. The method of  claim 1 , said kernel transform satisfying Mercer conditions. 
     
     
       3. The method of  claim 1 , said kernel transform comprising a radial basis function. 
     
     
       4. The method of  claim 1 , said applying a kernel transform comprising:
 assigning said transformed data to a first hidden layer of a neural network; 
 applying training data descriptors as weights of said first hidden layer of said neural network; and 
 calculating weights of a second hidden layer of said neural network numerically. 
 
     
     
       5. The method of  claim 1 , further comprising:
 classifying said transformed data using direct kernel partial least square (DK-PLS) machine learning. 
 
     
     
       6. The method of  claim 1 , said transforming said sensed data into said wavelet domain data comprising:
 applying a Daubechies wavelet transform to said sensed data. 
 
     
     
       7. The method of  claim 1 , further comprising:
 selecting features from said wavelet domain data which improve said classification of magnetocardiography data. 
 
     
     
       8. The method of  claim 1 , further comprising:
 normalizing said sensed data. 
 
     
     
       9. The method of  claim 8 , said normalizing said sensed data comprising:
 Mahalanobis scaling said sensed data. 
 
     
     
       10. The method of  claim 1 , further comprising:
 centering a kernel of said kernel transform. 
 
     
     
       11. An apparatus for automating the identification of meaningful features and the formulation of expert rules for classifying magnetocardiography data, comprising computerized storage, processing and programming for:
 applying a wavelet transform to sensed data acquired from sensors sensing magnetic fields generated by a patient's heart activity, resulting in wavelet domain data; 
 applying a direct kernel transform to said wavelet domain data, resulting in transformed data; and 
 identifying said meaningful features and formulating said expert rules from said transformed data, using machine learning. 
 
     
     
       12. The apparatus of  claim 11 , wherein kernel transform satisfies Mercer conditions. 
     
     
       13. The apparatus of  claim 11 , said kernel transform comprising a radial basis function. 
     
     
       14. The apparatus of  claim 11 , said computerized storage, processing and programming for applying a kernel transform further comprising computerized storage, processing and programming for:
 assigning said transformed data to a first hidden layer of a neural network; 
 applying training data descriptors as weights of said first hidden layer of said neural network; and 
 calculating weights of a second hidden layer of said neural network numerically. 
 
     
     
       15. The apparatus of  claim 11 , further comprising computerized storage, processing and programming for:
 classifying said transformed data using direct kernel partial least square (DK-PLS) machine learning. 
 
     
     
       16. The apparatus of  claim 11 , said computerized storage, processing and programming for transforming said sensed data into said wavelet domain data comprising computerized storage, processing and programming for:
 applying a Daubechies wavelet transform to said sensed data. 
 
     
     
       17. The apparatus of  claim 11 , further computerized storage, processing and programming for:
 selecting features from said wavelet domain data which improve said classification of magnetocardiography data. 
 
     
     
       18. The apparatus of  claim 11 , further comprising computerized storage, processing and programming for:
 normalizing said sensed data. 
 
     
     
       19. The apparatus of  claim 18 , said computerized storage, processing and programming for normalizing said sensed data comprising computerized storage, processing and programming for:
 Mahalanobis scaling said sensed data. 
 
     
     
       20. The apparatus of  claim 11 , further comprising computerized storage, processing and programming for:
 centering a kernel of said kernel transform.